This piece is part of an assignment for CASA0005 “Geographic Information Systems and Science”.
London is one of the least happy regions of the UK, despite having the highest per capita income, population density, and better health (Greater London Authority, 2012). This report addresses the need to consider an array of indicators to better represent and understand variation of well-being across urban space, to support targeted policy measures and enhance citizens’ lives. This study was designed to examine the relationships between well-being scores in London wards and their median household income and population density, and examine these variables’ potential to enhance the well-being measuring index. Linear regression models revealed positive and statistically significant associations between higher household income, lower population density and well-being scores. As Moran’s I, revealed statistically significant clustering of the residuals of this model, a Geographically Weighted Regression (GWR) model was calculated, to adjust for underlying spatial processes within the data and investigate the geographic variation in the association between household income, population density and wellbeing. The GWR revealed that an increase in a household income was associated with a statistically significant increase of the well-being score. This therefore provides some support for the integration of this variable in the calculation of the well-being index. The impact of population density on well-being was not consistent across London wards, but having a higher population density generally affected wards’ well-being scores negatively, but this association would benefit further analysis. The findings of this study have potential to enhance the index used to measure well-being across London.
Despite being under the spotlight within policy work, and a growing area of research, there is no consensus on a definition of well-being. Most philosophers and authors would characterize well-being as a general good feeling, and satisfaction in multiple domains of life, including housing, income, jobs, community, education, environment, governance, emotional and physical health, life satisfaction, safety, and work-life balance (Adler and Seligman, 2016). Dodge et al (2012) argue that well-being is an equilibrium, or the fluctuating state between challenges and resources in their physical, social, and psychological forms.
Correspondingly, there are many ways of measuring well-being, and a multitude of possible indicators to consider. Overall, research has shown that well-being correlates well with objective measures such as income and employment status, but these alone cannot tell the whole story (Greater London Authority, 2012). Therefore, measures of urban well-being typically combine a multitude of objective, subjective and built environment indicators which are selected to answer a specific purpose. The Healthy Cities Index contains 10 environment categories and 58 indicators and aims to shed light onto the links between the environment and health, as well as identify responsibility, so as to eventually promote well-being (Pineo, et al., 2018). For example, the City Well-Being Index rates the quality of life and liveability of the world’s major cities for the use of individuals (Harley and Knight, 2020).
As researchers establish links between people’s socio-spatial environments and their well-being, they call for a wider use of their findings in planning, policy, and decision-making (Rajendran et al., 2020). Indeed, city planners can utilise a measure of well-being as a baseline for tracking changes over time, for understanding what needs addressing in specific demographic groups, to determine how to allocate resources, to forecast future behaviour, and generally to elevate the human condition (Veneri and Edzes, 2017, Adler and Seligman, 2016; OECD, 2013; Greater London Authority, 2012).
The Office for National Statistics launched a specific program focusing on measuring national well-being in 2010, in order to comprehensively assess how development is affecting the population, and how sustainable it is for the future (ONS, 2019). Also, it is important for London policy makers to consider well-being at a small-scale level because of the often huge differences within boroughs. Greater London Authority (2012) created a tool which presents a combined measure of 12 of the most relevant ‘objective’ and ‘subjective’ indicators representing different areas of life, as per Table 1, to calculate a single well-being score at ward level (London DataStore, 2013).
Household income is an objective measure which correlates highly with well-being (Greater London Authority, 2012). However, household income was not directly included in the index, whereas authors and GLA suggest it could explain some of the well-being variation (Ballas, 2013; Stanca, 2010). The role of population density seems to have an ambivalent impact on well-being. They are generally well connected, and provide good access to services, but their residents are more likely to feel unsafe, have fewer social interactions and a lower access to quality green space (Dempsey et al, 2012). Rajendran et al (2020) examined the impact of population density and deprivation on the well-being of selected wards in Birmingham, UK, and found that in relatively less dense and deprived areas people felt better, which indicates population density is a meaningful indicator, but it may not give the full picture on its own. Li and Kanazawa (2016) found population density at the census block to decrease self-reported life satisfaction. There is no consensus on an ideal level of population density, or the set of conditions that need to be satisfied for improved well-being, but further analysis of this phenomenon may enlighten this relationship. It seems both household income and population density could have a significant association with well-being, and bear potential for a more accurate measure of it.
This report aims to: (1) examine spatial distribution of well-being across London and spot any clusters of high or low scores; (2) investigate inequalities in terms of how inhabitants of different wards are likely to experience well-being; (3) analyse how indicators that were not included in the GLA well-being index can enhance its calculation.
The well-being and household income files downloaded from London Data Store were .xls and .xlsx spreadsheets. Some metadata, explanations, titles, empty rows and columns were manually removed before the files were saved as .csv. Raw and cleaned datasets and Readme files can be found on the linked GitHub repository. It was possible to add weighting to each of the 12 indicators of the well-being index, depending on what one considers to be the more or less important and generate bespoke scores, but for the sake of this analysis all weightings were set equal. The rest of the data cleaning and all of the analysis and map making were done in RStudio to foster scientific reproducibility. For this analysis, the most recent well-being scores data available, dating from 2013, was used. Median Household Income was chosen over Mean Household Income as it seemed better for population representativity purposes; it is a yearly measure for the 2012/2013 period.
For the analysis of spatial autocorrelation, queen-based contiguity weights were adopted for Global Moran’s I as well as subsequent spatial analyses because the sample size seemed big enough (n=625). Breaks for Gi* Statistics were set based on confidence levels related to data points’ distance from the mean.
Then, a linear model was used to assess the relationships between the independent variables, the household median income and the population density and the well-being scores. It predicts the well-being score of wards and includes median household income and population density in the calculation. As part of the Ordinary Least Square (OLS) model, the residuals indicate the distance between the values predicted by this model and the original well-being scores. The residual errors were also investigated with Moran’s I, revealing significant spatial clustering, and highlighting how the model systematically over- and under-estimates the associations, implying geographic variation across the study space.
As with previous policy-oriented research of the environment and human life characteristics, a Geographically Weighted Regression (GWR) model was selected as an appropriate method to analyse local variations (Houlden et al., 2019; Chen and Truong, 2012; Ogneva-Himmelberger et al, 2009). Its use in this study aims to adjust for these evident underlying spatial processes within the data and investigate the geographic variation in the association between household income, population density and well-being. The GWR method calculates a localised regression using distance-based weighting for each point, this method is essentially a regression model in which the coefficients are allowed to vary over space (Brunsdon et al., 1996). The GWR coefficients’ strengths and spatial variation imply that the importance of household income and population density may also differ across the city. The GWR is supported by a local weight matrix W, calculated from a kernel function that places more weight on neighbouring locations and takes into account the degree of spatial dependence in a continuous spatial framework (Fotheringham et al., 2003). Since there is no consensus on how to assess confidence in the coefficients from a GWR model, this study adopted the ArcGIS Pro (n.d.) approach, dividing the coefficient by the standard error provided for each feature as a way of scaling the magnitude of the estimation.
As shown in Figure 1, there was some variation in the spatial distribution of the 2013 well-being scores across London wards. The southern and south-western outer-edges of London had relatively higher well-being scores, while wards in north-east London had relatively lower scores. The well-being scores in central London were not homogenous, with the east getting more negative scores, and the west, around Belgravia and Kensington, getting some of the highest scores.
## Reading layer `London_Ward_CityMerged' from data source `C:\Users\Utilisateur\Documents\UCL_MSc_SGDS\T1_CASA0005_GISScience\week8\onlinemapping_Rmd\Assignment_final\data\statistical-gis-boundaries-london\ESRI\London_Ward_CityMerged.shp' using driver `ESRI Shapefile'
## Simple feature collection with 625 features and 7 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 503568.2 ymin: 155850.8 xmax: 561957.5 ymax: 200933.9
## projected CRS: OSGB 1936 / British National Grid## tmap mode set to interactive viewing## Variable(s) "WB_score_2013" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.A Global Moran’s I test calculated a statistic of 0.59 with a p-value < 2.2e-16, showing that similar well-being score values were distinctively clustered together. The G*i statistic, was calculated and mapped to identify statistical clustering of high values or low values and highlight hot and cold spots of well-being. Areas in red on Figure 2, on the south-western edge of London are pockets of high well-being scores, while the areas in blue in the North-East are clusters of neighbouring wards with low well-being scores.
## Warning in st_centroid.sf(.): st_centroid assumes attributes are constant over geometries of x##
## Moran I test under randomisation
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## data: .
## weights: Lward.lw
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## Moran I statistic standard deviate = 25.715, p-value < 2.2e-16
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 0.594993366 -0.001602564 0.000538256## tmap mode set to interactive viewing## Variable(s) "density_G" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.